Neural Cross-Lingual Transfer and Limited Annotated Data for Named Entity Recognition in Danish
Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-review
Open access
Publication Information
Output type
Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-reviewOriginal language
EnglishPublication milestones
- Published - 2019
Publication status
Published - 2019
Publisher
Association for Computational Linguistics, United StatesBook series
- Book series name: NEALT (Northern European Association of Language Technology) Proceedings Series
ISSN: 1736-6305
ISBN (Electronic)
978-91-7929-995-8Host publication title
Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa’19) .Abstract
Named Entity Recognition (NER) has greatly advanced by the introduction
of deep neural architectures. However, the success of these methods
depends on large amounts of training data. The scarcity of publicly available human-labeled datasets has resulted in limited evaluation of existing NER systems, as is the case for Danish. This paper studies the effectiveness of cross-lingual transfer for
Danish, evaluates its complementarity to limited gold data, and sheds light on
performance of Danish NER.
of deep neural architectures. However, the success of these methods
depends on large amounts of training data. The scarcity of publicly available human-labeled datasets has resulted in limited evaluation of existing NER systems, as is the case for Danish. This paper studies the effectiveness of cross-lingual transfer for
Danish, evaluates its complementarity to limited gold data, and sheds light on
performance of Danish NER.
Access to documents
Accepted author manuscript, 93.82 KB